[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-231889-113":53,"doc-detail-231889-id":128},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":121,"head_meta":123,"extra_data":125,"updated_unix":127},113,"id","socio-economic-data-clustering-using-the-k-means-algorithm","Klasterisasi Data Sosial Ekonomi Menggunakan Algoritma K-Means","","Kemajuan teknologi informasi mendorong peningkatan volume data sosial ekonomi yang mencakup aspek pendidikan dan kebutuhan pokok masyarakat. Penelitian ini mengelompokkan data sosial ekonomi menggunakan algoritma K-Means melalui dua fokus analisis: jumlah peminat pada lembaga pendidikan tinggi (PTN dan PTS) serta harga rata-rata bahan pokok di Kota Palembang. Normalisasi dilakukan dengan Min-Max Scaling, kemudian K-Means diterapkan dengan k=2 untuk data pendidikan dan k=3 untuk data harga, menghasilkan klaster peminat serta klaster harga tinggi, menengah, dan rendah sebagai dasar kebijakan berbasis data.",{"@graph":63,"@context":120},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/socio-economic-data-clustering-using-the-k-means-algorithm/231889/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/socio-economic-data-clustering-using-the-k-means-algorithm/231889.png","ImageObject",300,407,{"name":89,"@type":90},"Finn","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-29","2026-09-10",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",6,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Penelitian ini menggunakan data apa saja untuk klasterisasi sosial ekonomi?","Question",{"text":110,"@type":111},"Penelitian menggunakan dua jenis data: jumlah peminat lembaga pendidikan tinggi (PTN dan PTS) dan harga rata-rata bahan pokok di Kota Palembang.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana proses pengelompokan dilakukan pada penelitian ini?",{"text":115,"@type":111},"Data dinormalisasi menggunakan Min-Max Scaling, kemudian diterapkan algoritma K-Means dengan k=2 untuk data pendidikan dan k=3 untuk data harga bahan pokok.",{"name":117,"@type":108,"acceptedAnswer":118},"Apa hasil utama klasterisasi pada data pendidikan dan data harga bahan pokok?",{"text":119,"@type":111},"Pada data pendidikan, universitas masuk klaster peminat tertinggi, sedangkan lembaga lainnya berada pada klaster menengah hingga rendah. Pada data bahan pokok, terbentuk tiga klaster harga: tinggi, menengah, dan rendah.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},231889,1789067678,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":137,"language":138,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":61,"update_tm":127,"read_time":142},34359740700684,"https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487","e-ISSN: 2987-1891  \nAvailable online at [http://pcinformatika.org/index.php/pcif/index](http://pcinformatika.org/index.php/pcif/index)  \nJournal on Pustaka Cendekia Informatika  \nVolume 2 No. 1, Februari-Mei 2024, pp 12-18  \nKlasterisasi Data Sosial Ekonomi Menggunakan Algoritma K-Means  \nTiyo Wahyudi¹*, Miftahul Jannah², Bapak Zurnan Alfian3  \n1,2,3 Program Studi Teknik Informatika, Universitas Pamulang, Jl. Suryakencana No. 1, Pamulang Bar., Kec. Pamulang, Kota Tangerang Selatan, Banten.  \nE-mail: [tyowhydi@gmail.com](tyowhydi@gmail.com)  \n* Corresponding Author  \n[https://doi.org/](https://doi.org/)  \nARTICLE INFO ABSTRACT  \nArticle history  \nReceived: 24 January 2024  \nRevised: 30 January 2024  \nAccepted: 5 February 2024  \nKata Kunci  \nK-Means, Klasterisasi, Data Sosial Ekonomi, Pendidikan Tinggi, Harga Bahan Pokok.  \nKeywords  \nK-Means, Clustering, SocioEconomic Data, Higher Education, Basic Commodity Prices.  \nPerkembangan teknologi informasi telah mendorong peningkatan volume data sosial ekonomi yang mencakup berbagai aspek seperti pendidikan dankebutuhan pokok masyarakat. Penelitian ini bertujuan untuk mengelompokkan data sosial ekonomi menggunakan algoritma K-Means, dengan fokus pada dua jenis data: jumlah peminat terhadap lembaga pendidikan tinggi (PTN dan PTS) serta harga rata-rata bahan pokok di Kota Palembang. Dataset pertama diperoleh dari Statistik Pendidikan Tinggi Indonesia (Depdiknas 2006) yang mencakup lima kategori lembaga: universitas, institut, sekolah tinggi, akademi, dan politeknik. Dataset kedua diambil dari Tabel 8.2 Statistik BPS tahun 2004– 2005 yang mencatat harga komoditas seperti daging sapi, ayam ras, beras, gulapasir, telur ayam ras, dan minyak goreng curah. Proses klasterisasi dilakukandengan normalisasi data menggunakan metode Min-Max Scaling, diikuti oleh penerapan algoritma K-Means dengan jumlah klaster masing-masing k = 2 untuk data pendidikan dan k = 3 untuk data harga bahan pokok. Hasil penelitian menunjukkan bahwa universitas tergolong dalam klaster peminat tertinggi, sedangkan lembaga lainnya berada pada klaster menengah hingga rendah. Pada data bahan pokok, diperoleh tiga klaster harga: tinggi, menengah, dan rendah. Temuan ini diharapkan dapat menjadi dasar dalam perumusan kebijakan di sektor pendidikan dan pengendalian harga bahan kebutuhan pokok secara lebih terarah dan berbasis data.  \nThe advancement of information technology has driven a significant increase in the volume of socio-economic data, encompassing various aspects such as education and essential community needs. This study aims to cluster socioeconomic data using the K-Means algorithm, focusing on two types of data: the number of applicants to higher education institutions (public and private universities) and the average prices of basic commodities in Palembang City. The first dataset was obtained from the Indonesian Higher Education Statistics (Depdiknas 2006), covering five categories of institutions: universities, institutes, colleges, academies, and polytechnics. The second dataset was taken from Table 8.2 of the Consumer Price Statistics by BPSfor the years 2004–  \n2005, which includes prices of commodities such as beef, broiler chicken, rice, granulated sugar, chicken eggs, and bulk cooking oil. The clustering process was carried out by normalizing the data using the Min-Max Scaling method, followed by the application of the K-Means algorithm with k = 2 clusters for  \neducational data and k = 3 for commodity price data. The results showed that universities fall into the highest applicant cluster, while other institutions are grouped into medium to low clusters. In the commodity dataset, three price clusters were formed: high, medium, and low. These findings are expected to serve as a foundation for policy formulation in the education sector and for price control of essential goods in a more targeted and data-driven manner.  \nThis is an open access article under the CC–BY-SA license.   \nKlasterisasi Data Sosial Ek","cbCaibUhgjOzM592","https://ap.wps.com/l/cbCaibUhgjOzM592","pdf",595289,7,"Indonesian","# PENDAHULUAN\n## Klasterisasi\n## Tujuan dan Manfaat Penelitian","[{\"question\":\"Penelitian ini menggunakan data apa saja untuk klasterisasi sosial ekonomi?\",\"answer\":\"Penelitian menggunakan dua jenis data: jumlah peminat lembaga pendidikan tinggi (PTN dan PTS) dan harga rata-rata bahan pokok di Kota Palembang.\"},{\"question\":\"Bagaimana proses pengelompokan dilakukan pada penelitian ini?\",\"answer\":\"Data dinormalisasi menggunakan Min-Max Scaling, kemudian diterapkan algoritma K-Means dengan k=2 untuk data pendidikan dan k=3 untuk data harga bahan pokok.\"},{\"question\":\"Apa hasil utama klasterisasi pada data pendidikan dan data harga bahan pokok?\",\"answer\":\"Pada data pendidikan, universitas masuk klaster peminat tertinggi, sedangkan lembaga lainnya berada pada klaster menengah hingga rendah. Pada data bahan pokok, terbentuk tiga klaster harga: tinggi, menengah, dan rendah.\"}]","Klasterisasi Data Sosial Ekonomi Menggunakan Algoritma K-Means | PDF",11]